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Archive for the ‘Population Health Management, Genetics & Pharmaceutical’ Category


Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Research about marijuana and fertility is limited but some previous studies suggested that it might harm semen quality. Smoking of any type is also known to be a risk factor for male infertility. So, men who have smoked cannabis are expected to have worse measures of fertility but the data from a recent study suggested the opposite. The finding contradicts all conventional knowledge on how weed affects sperm. This may be because previous research typically focused on men with drug abuse history but this present study simply asked men if they had smoked more than two joints in their life.

 

Analysis of 1,143 semen samples from 662 men collected between 2000 and 2017 at the Fertility Clinic at Massachusetts General Hospital showed that those who had smoked weed at some point in their life had a mean sperm concentration of 62.7 million sperm per milliliter (mL) of ejaculate, while men who avoided marijuana entirely had mean concentrations of 45.4 million/mL. Added to this only 5% of weed smokers had sperm concentrations below the 15 million/mL threshold the World Health Organization has set for a “normal” sperm count, versus 12% of men who never smoked marijuana.

 

The study has some imperfections such as the participants are not necessarily representative of the general population. They were predominantly college educated men with a mean age of 36, and were all seeking treatment at a fertility center. Further research is needed to support the findings. Two possibilities are put forward by the researchers as the reason behind such data. The first is that low levels of marijuana could have a positive effect on the endocannabinoid system, the neurotransmitters in the nervous system that bind to cannabinoid receptors, and are known to regulate fertility. The second is that may be weed-smokers are just bigger risk takers and men with higher testosterone levels and thus have better sperm count.

 

But, there’s certainly no medical recommendation to smoke weed as a fertility treatment but this study, at least, suggests that a little marijuana doesn’t hurt and might benefit sperm production in some way. But, the researchers specified that their finding does not necessarily mean that smoking cannabis increases the chances of fatherhood.

 

References:

 

https://www.ncbi.nlm.nih.gov/pubmed/30726923

 

https://www.bloomberg.com/amp/news/articles/2019-02-06/cannabis-smoking-associated-with-higher-sperm-count-study-finds?__twitter_impression=true

 

https://qz.com/1543564/smoking-weed-linked-to-higher-sperm-count-in-a-harvard-study/

 

https://www.thestar.com.my/news/world/2019/02/06/cannabis-smoking-associated-with-higher-sperm-count-study-finds/

 

http://time.com/5520421/smoking-marijuana-sperm-fertility/

 

https://www.health.com/infertility/marijuana-sperm-count

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Stroke is a leading cause of death worldwide and the most common cause of long-term disability amongst adults, more particularly in patients with diabetes mellitus and arterial hypertension. Increasing evidence suggests that disordered physiological variables following acute ischaemic stroke, especially hyperglycaemia, adversely affect outcomes.

 

Post-stroke hyperglycaemia is common (up to 50% of patients) and may be rather prolonged, regardless of diabetes status. A substantial body of evidence has demonstrated that hyperglycaemia has a deleterious effect upon clinical and morphological stroke outcomes. Therefore, hyperglycaemia represents an attractive physiological target for acute stroke therapies.

 

However, whether intensive glycaemic manipulation positively influences the fate of ischaemic tissue remains unknown. One major adverse event of management of hyperglycaemia with insulin (either glucose-potassium-insulin infusions or intensive insulin therapy) is the occurrence of hypoglycaemia, which can also induce cerebral damage.

 

Doctors all over the world have debated whether intensive glucose management, which requires the use of IV insulin to bring blood sugar levels down to 80-130 mg/dL, or standard glucose control using insulin shots, which aims to get glucose below 180 mg/dL, lead to better outcomes after stroke.

 

A period of hyperglycemia is common, with elevated blood glucose in the periinfarct period consistently linked with poor outcome in patients with and without diabetes. The mechanisms that underlie this deleterious effect of dysglycemia on ischemic neuronal tissue remain to be established, although in vitro research, functional imaging, and animal work have provided clues.

 

While prompt correction of hyperglycemia can be achieved, trials of acute insulin administration in stroke and other critical care populations have been equivocal. Diabetes mellitus and hyperglycemia per se are associated with poor cerebrovascular health, both in terms of stroke risk and outcome thereafter.

 

Interventions to control blood sugar are available but evidence of cerebrovascular efficacy are lacking. In diabetes, glycemic control should be part of a global approach to vascular risk while in acute stroke, theoretical data suggest intervention to lower markedly elevated blood glucose may be of benefit, especially if thrombolysis is administered.

 

Both hypoglycaemia and hyperglycaemia may lead to further brain injury and clinical deterioration; that is the reason these conditions should be avoided after stroke. Yet, when correcting hyperglycaemia, great care should be taken not to switch the patient into hypoglycaemia, and subsequently aggressive insulin administration treatment should be avoided.

 

Early identification and prompt management of hyperglycaemia, especially in acute ischaemic stroke, is recommended. Although the appropriate level of blood glucose during acute stroke is still debated, a reasonable approach is to keep the patient in a mildly hyperglycaemic state, rather than risking hypoglycaemia, using continuous glucose monitoring.

 

The primary results from the Stroke Hyperglycemia Insulin Network Effort (SHINE) study, a large, multisite clinical study showed that intensive glucose management did not improve functional outcomes at 90 days after stroke compared to standard glucose therapy. In addition, intense glucose therapy increased the risk of very low blood glucose (hypoglycemia) and required a higher level of care such as increased supervision from nursing staff, compared to standard treatment.

 

References:

 

https://www.nih.gov/news-events/news-releases/nih-study-provides-answer-long-held-debate-blood-sugar-control-after-stroke

 

https://www.ncbi.nlm.nih.gov/pubmed/27873213

 

https://www.ncbi.nlm.nih.gov/pubmed/19342845

 

https://www.ncbi.nlm.nih.gov/pubmed/20491782

 

https://www.ncbi.nlm.nih.gov/pubmed/21211743

 

https://www.ncbi.nlm.nih.gov/pubmed/18690907

 

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Hypertriglyceridemia: Evaluation and Treatment Guideline

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Severe and very severe hypertriglyceridemia increase the risk for pancreatitis, whereas mild or moderate hypertriglyceridemia may be a risk factor for cardiovascular disease. Individuals found to have any elevation of fasting triglycerides should be evaluated for secondary causes of hyperlipidemia including endocrine conditions and medications. Patients with primary hypertriglyceridemia must be assessed for other cardiovascular risk factors, such as central obesity, hypertension, abnormalities of glucose metabolism, and liver dysfunction. The aim of this study was to develop clinical practice guidelines on hypertriglyceridemia.

The diagnosis of hypertriglyceridemia should be based on fasting levels, that mild and moderate hypertriglyceridemia (triglycerides of 150–999 mg/dl) be diagnosed to aid in the evaluation of cardiovascular risk, and that severe and very severe hypertriglyceridemia (triglycerides of >1000 mg/dl) be considered a risk for pancreatitis. The patients with hypertriglyceridemia must be evaluated for secondary causes of hyperlipidemia and that subjects with primary hypertriglyceridemia be evaluated for family history of dyslipidemia and cardiovascular disease.

The treatment goal in patients with moderate hypertriglyceridemia should be a non-high-density lipoprotein cholesterol level in agreement with National Cholesterol Education Program Adult Treatment Panel guidelines. The initial treatment should be lifestyle therapy; a combination of diet modification, physical activity and drug therapy may also be considered. In patients with severe or very severe hypertriglyceridemia, a fibrate can be used as a first-line agent for reduction of triglycerides in patients at risk for triglyceride-induced pancreatitis.

Three drug classes (fibrates, niacin, n-3 fatty acids) alone or in combination with statins may be considered as treatment options in patients with moderate to severe triglyceride levels. Statins are not be used as monotherapy for severe or very severe hypertriglyceridemia. However, statins may be useful for the treatment of moderate hypertriglyceridemia when indicated to modify cardiovascular risk.

 

References:

 

https://www.medpagetoday.com/clinical-connection/cardio-endo/77242?xid=NL_CardioEndoConnection_2019-01-21

https://www.ncbi.nlm.nih.gov/pubmed/19307519

https://www.ncbi.nlm.nih.gov/pubmed/23009776

https://www.ncbi.nlm.nih.gov/pubmed/6827992

https://www.ncbi.nlm.nih.gov/pubmed/22463676

https://www.ncbi.nlm.nih.gov/pubmed/17635890

 

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Morbid Obesity linked to socioeconomic status was in a Twin Pairs Research on effects of genes and environment in 560 common conditions – Disease and Health by ZIP code (Environment) and genetic code (Human Genome)

Reporter: Aviva Lev-Ari, PhD, RN

 

  • Senior study author Chirag Patel, assistant professor of biomedical informatics in the Blavatnik Institute at HMS.
  • Co-investigators were Braden Tierney and Arjun Manrai of Harvard Medical School, and
  • Jian Yang and Peter Visscher of the University of Queensland, Australia.

Data sets for the study were provided by Aetna insurance company. Aetna had no funding role in the study. The research was supported by the Australian National Health and Medical Research Council (grants 1078037 and 1113400), National Science Foundation (grant 1636870), and Sylvia and Charles Viertel Charitable Foundation.

 

Detailed study results available here: http://apps.chiragjpgroup.org/catch/

Conclusions 

Nearly 40 percent of the diseases in the study (225 of 560) had a genetic component, while 25 percent (138) were driven at least in part by factors stemming from sharing the same household, social influences, and the like. Cognitive disorders demonstrated the greatest degree of heritability — four out of five diseases showed a genetic component — while connective tissue diseases had the lowest degree of genetic influence. Of all disease categories, eye disorders carried the highest degree of environmental influence, with 27 of 42 diseases showing such effect. They were followed by respiratory diseases, with 34 out of 48 conditions showing an effect from sharing a household. The disease category with lowest environmental influence was reproductive illnesses, with three of 18 conditions showing such effect, and cognitive conditions, with two out of five showing an influence.

Overall, socioeconomic status, climate conditions, and air quality in each twin pair’s ZIP code had a far weaker effect on disease than genes and shared environment — a composite measure of external, nongenetic influences including family and lifestyle, household, and neighborhood.

In total, 145 of 560 diseases were modestly influenced by socio-economic status derived by ZIP code. Thirty-six diseases were influenced at least in part by air quality, and 117 were affected by changes in temperature. The condition with the strongest potential link to socioeconomic status was morbid obesity. While obesity undoubtedly has a genetic component, the researchers said, the findings raise an important question about the influence of environment on genetic predispositions.

“This finding opens up a whole slew of questions, including whether and how a change in socioeconomic status and lifestyle might compare against genetic predisposition to obesity,” Patel said.

Lead poisoning was, not surprisingly, entirely driven by environment. Conditions such as flu and Lyme disease were, again unsurprisingly, affected by differences in climate.

When researchers looked at classes of diseases by monthly health care spending, they found that both genes and environment significantly contributed to cost of care, with the two being nearly equal drivers of spending. Almost 60 percent of monthly health spending could be predicted by analyzing genetic and environmental factors.

Large-scale analysis like this study can help forecast long-term spending for various conditions and inform resource allocation and policy decisions, the researchers said.

Detailed study results available here: http://apps.chiragjpgroup.org/catch/

SOURCE

http://apps.chiragjpgroup.org/catch/

https://news.harvard.edu/gazette/story/2019/01/researchers-able-to-determine-the-effects-of-genes-and-environment-in-560-common-conditions/?utm_source=SilverpopMailing&utm_medium=email&utm_campaign=Daily%20Gazette%202%20stories%201%20event%20(no%20Seen%20or%20Heard)%20(1)

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Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

Curator: Stephen J. Williams, Ph.D.

Updated 12/18/2018

In the efforts to reduce healthcare costs, provide increased accessibility of service for patients, and drive biomedical innovations, many healthcare and biotechnology professionals have looked to advances in digital technology to determine the utility of IT to drive and extract greater value from healthcare industry.  Two areas of recent interest have focused how best to use blockchain and artificial intelligence technologies to drive greater efficiencies in our healthcare and biotechnology industries.

More importantly, with the substantial increase in ‘omic data generated both in research as well as in the clinical setting, it has become imperative to develop ways to securely store and disseminate the massive amounts of ‘omic data to various relevant parties (researchers or clinicians), in an efficient manner yet to protect personal privacy and adhere to international regulations.  This is where blockchain technologies may play an important role.

A recent Oncotarget paper by Mamoshina et al. (1) discussed the possibility that next-generation artificial intelligence and blockchain technologies could synergize to accelerate biomedical research and enable patients new tools to control and profit from their personal healthcare data, and assist patients with their healthcare monitoring needs. According to the abstract:

The authors introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship value of the data.  They also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare.  In this system, blockchain and deep learning technologies would provide the secure and transparent distribution of personal data in a healthcare marketplace, and would also be useful to resolve challenges faced by the regulators and return control over personal data including medical records to the individual.

The review discusses:

  1. Recent achievements in next-generation artificial intelligence
  2. Basic concepts of highly distributed storage systems (HDSS) as a preferred method for medical data storage
  3. Open source blockchain Exonium and its application for healthcare marketplace
  4. A blockchain-based platform allowing patients to have control of their data and manage access
  5. How advances in deep learning can improve data quality, especially in an era of big data

Advances in Artificial Intelligence

  • Integrative analysis of the vast amount of health-associated data from a multitude of large scale global projects has proven to be highly problematic (REF 27), as high quality biomedical data is highly complex and of a heterogeneous nature, which necessitates special preprocessing and analysis.
  • Increased computing processing power and algorithm advances have led to significant advances in machine learning, especially machine learning involving Deep Neural Networks (DNNs), which are able to capture high-level dependencies in healthcare data. Some examples of the uses of DNNs are:
  1. Prediction of drug properties(2, 3) and toxicities(4)
  2. Biomarker development (5)
  3. Cancer diagnosis (6)
  4. First FDA approved system based on deep learning Arterys Cardio DL
  • Other promising systems of deep learning include:
    • Generative Adversarial Networks (https://arxiv.org/abs/1406.2661): requires good datasets for extensive training but has been used to determine tumor growth inhibition capabilities of various molecules (7)
    • Recurrent neural Networks (RNN): Originally made for sequence analysis, RNN has proved useful in analyzing text and time-series data, and thus would be very useful for electronic record analysis. Has also been useful in predicting blood glucose levels of Type I diabetic patients using data obtained from continuous glucose monitoring devices (8)
    • Transfer Learning: focused on translating information learned on one domain or larger dataset to another, smaller domain. Meant to reduce the dependence on large training datasets that RNN, GAN, and DNN require.  Biomedical imaging datasets are an example of use of transfer learning.
    • One and Zero-Shot Learning: retains ability to work with restricted datasets like transfer learning. One shot learning aimed to recognize new data points based on a few examples from the training set while zero-shot learning aims to recognize new object without seeing the examples of those instances within the training set.

Highly Distributed Storage Systems (HDSS)

The explosion in data generation has necessitated the development of better systems for data storage and handling. HDSS systems need to be reliable, accessible, scalable, and affordable.  This involves storing data in different nodes and the data stored in these nodes are replicated which makes access rapid. However data consistency and affordability are big challenges.

Blockchain is a distributed database used to maintain a growing list of records, in which records are divided into blocks, locked together by a crytosecurity algorithm(s) to maintain consistency of data.  Each record in the block contains a timestamp and a link to the previous block in the chain.  Blockchain is a distributed ledger of blocks meaning it is owned and shared and accessible to everyone.  This allows a verifiable, secure, and consistent history of a record of events.

Data Privacy and Regulatory Issues

The establishment of the Health Insurance Portability and Accountability Act (HIPAA) in 1996 has provided much needed regulatory guidance and framework for clinicians and all concerned parties within the healthcare and health data chain.  The HIPAA act has already provided much needed guidance for the latest technologies impacting healthcare, most notably the use of social media and mobile communications (discussed in this article  Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.).  The advent of blockchain technology in healthcare offers its own unique challenges however HIPAA offers a basis for developing a regulatory framework in this regard.  The special standards regarding electronic data transfer are explained in HIPAA’s Privacy Rule, which regulates how certain entities (covered entities) use and disclose individual identifiable health information (Protected Health Information PHI), and protects the transfer of such information over any medium or electronic data format. However, some of the benefits of blockchain which may revolutionize the healthcare system may be in direct contradiction with HIPAA rules as outlined below:

Issues of Privacy Specific In Use of Blockchain to Distribute Health Data

  • Blockchain was designed as a distributed database, maintained by multiple independent parties, and decentralized
  • Linkage timestamping; although useful in time dependent data, proof that third parties have not been in the process would have to be established including accountability measures
  • Blockchain uses a consensus algorithm even though end users may have their own privacy key
  • Applied cryptography measures and routines are used to decentralize authentication (publicly available)
  • Blockchain users are divided into three main categories: 1) maintainers of blockchain infrastructure, 2) external auditors who store a replica of the blockchain 3) end users or clients and may have access to a relatively small portion of a blockchain but their software may use cryptographic proofs to verify authenticity of data.

 

YouTube video on How #Blockchain Will Transform Healthcare in 25 Years (please click below)

 

 

In Big Data for Better Outcomes, BigData@Heart, DO->IT, EHDN, the EU data Consortia, and yes, even concepts like pay for performance, Richard Bergström has had a hand in their creation. The former Director General of EFPIA, and now the head of health both at SICPA and their joint venture blockchain company Guardtime, Richard is always ahead of the curve. In fact, he’s usually the one who makes the curve in the first place.

 

 

 

Please click on the following link for a podcast on Big Data, Blockchain and Pharma/Healthcare by Richard Bergström:

References

  1. Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., Ogu, I. O., and Zhavoronkov, A. (2018) Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare, Oncotarget 9, 5665-5690.
  2. Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., and Zhavoronkov, A. (2016) Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data, Molecular pharmaceutics 13, 2524-2530.
  3. Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., and Lu, H. (2017) Deep-Learning-Based Drug-Target Interaction Prediction, Journal of proteome research 16, 1401-1409.
  4. Gao, M., Igata, H., Takeuchi, A., Sato, K., and Ikegaya, Y. (2017) Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds, Journal of pharmacological sciences 133, 70-78.
  5. Putin, E., Mamoshina, P., Aliper, A., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C., Vijg, J., and Zhavoronkov, A. (2016) Deep biomarkers of human aging: Application of deep neural networks to biomarker development, Aging 8, 1021-1033.
  6. Vandenberghe, M. E., Scott, M. L., Scorer, P. W., Soderberg, M., Balcerzak, D., and Barker, C. (2017) Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer, Scientific reports 7, 45938.
  7. Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., and Zhavoronkov, A. (2017) druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico, Molecular pharmaceutics 14, 3098-3104.
  8. Ordonez, F. J., and Roggen, D. (2016) Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors (Basel) 16.

Articles from clinicalinformaticsnews.com

Healthcare Organizations Form Synaptic Health Alliance, Explore Blockchain’s Impact On Data Quality

From http://www.clinicalinformaticsnews.com/2018/12/05/healthcare-organizations-form-synaptic-health-alliance-explore-blockchains-impact-on-data-quality.aspx

By Benjamin Ross

December 5, 2018 | The boom of blockchain and distributed ledger technologies have inspired healthcare organizations to test the capabilities of their data. Quest Diagnostics, in partnership with Humana, MultiPlan, and UnitedHealth Group’s Optum and UnitedHealthcare, have launched a pilot program that applies blockchain technology to improve data quality and reduce administrative costs associated with changes to healthcare provider demographic data.

The collective body, called Synaptic Health Alliance, explores how blockchain can keep only the most current healthcare provider information available in health plan provider directories. The alliance plans to share their progress in the first half of 2019.

Providing consumers looking for care with accurate information when they need it is essential to a high-functioning overall healthcare system, Jason O’Meara, Senior Director of Architecture at Quest Diagnostics, told Clinical Informatics News in an email interview.

“We were intentional about calling ourselves an alliance as it speaks to the shared interest in improving health care through better, collaborative use of an innovative technology,” O’Meara wrote. “Our large collective dataset and national footprints enable us to prove the value of data sharing across company lines, which has been limited in healthcare to date.”

O’Meara said Quest Diagnostics has been investing time and resources the past year or two in understanding blockchain, its ability to drive purpose within the healthcare industry, and how to leverage it for business value.

“Many health care and life science organizations have cast an eye toward blockchain’s potential to inform their digital strategies,” O’Meara said. “We recognize it takes time to learn how to leverage a new technology. We started exploring the technology in early 2017, but we quickly recognized the technology’s value is in its application to business to business use cases: to help transparently share information, automate mutually-beneficial processes and audit interactions.”

Quest began discussing the potential for an alliance with the four other companies a year ago, O’Meara said. Each company shared traits that would allow them to prove the value of data sharing across company lines.

“While we have different perspectives, each member has deep expertise in healthcare technology, a collaborative culture, and desire to continuously improve the patient/customer experience,” said O’Meara. “We also recognize the value of technology in driving efficiencies and quality.”

Following its initial launch in April, Synaptic Health Alliance is deploying a multi-company, multi-site, permissioned blockchain. According to a whitepaper published by Synaptic Health, the choice to use a permissioned blockchain rather than an anonymous one is crucial to the alliance’s success.

“This is a more effective approach, consistent with enterprise blockchains,” an alliance representative wrote. “Each Alliance member has the flexibility to deploy its nodes based on its enterprise requirements. Some members have elected to deploy their nodes within their own data centers, while others are using secured public cloud services such as AWS and Azure. This level of flexibility is key to growing the Alliance blockchain network.”

As the pilot moves forward, O’Meara says the Alliance plans to open ability to other organizations. Earlier this week Aetna and Ascension announced they joined the project.

“I am personally excited by the amount of cross-company collaboration facilitated by this project,” O’Meara says. “We have already learned so much from each other and are using that knowledge to really move the needle on improving healthcare.”

 

US Health And Human Services Looks To Blockchain To Manage Unstructured Data

http://www.clinicalinformaticsnews.com/2018/11/29/us-health-and-human-services-looks-to-blockchain-to-manage-unstructured-data.aspx

By Benjamin Ross

November 29, 2018 | The US Department of Health and Human Services (HHS) is making waves in the blockchain space. The agency’s Division of Acquisition (DA) has developed a new system, called Accelerate, which gives acquisition teams detailed information on pricing, terms, and conditions across HHS in real-time. The department’s Associate Deputy Assistant Secretary for Acquisition, Jose Arrieta, gave a presentation and live demo of the blockchain-enabled system at the Distributed: Health event earlier this month in Nashville, Tennessee.

Accelerate is still in the prototype phase, Arrieta said, with hopes that the new system will be deployed at the end of the fiscal year.

HHS spends around $25 billion a year in contracts, Arrieta said. That’s 100,000 contracts a year with over one million pages of unstructured data managed through 45 different systems. Arrieta and his team wanted to modernize the system.

“But if you’re going to change the way a workforce of 20,000 people do business, you have to think your way through how you’re going to do that,” said Arrieta. “We didn’t disrupt the existing systems: we cannibalized them.”

The cannibalization process resulted in Accelerate. According to Arrieta, the system functions by creating a record of data rather than storing it, leveraging machine learning, artificial intelligence (AI), and robotic process automation (RPA), all through blockchain data.

“We’re using that data record as a mechanism to redesign the way we deliver services through micro-services strategies,” Arrieta said. “Why is that important? Because if you have a single application or data use that interfaces with 55 other applications in your business network, it becomes very expensive to make changes to one of the 55 applications.”

Accelerate distributes the data to the workforce, making it available to them one business process at a time.

“We’re building those business processes without disrupting the existing systems,” said Arrieta, and that’s key. “We’re not shutting off those systems. We’re using human-centered design sessions to rebuild value exchange off of that data.”

The first application for the system, Arrieta said, can be compared to department stores price-matching their online competitors.

It takes the HHS close to a month to collect the amalgamation of data from existing system, whether that be terms and conditions that drive certain price points, or software licenses.

“The micro-service we built actually analyzes that data, and provides that information to you within one second,” said Arrieta. “This is distributed to the workforce, to the 5,000 people that do the contracting, to the 15,000 people that actually run the programs at [HHS].”

This simple micro-service is replicated on every node related to HHS’s internal workforce. If somebody wants to change the algorithm to fit their needs, they can do that in a distributed manner.

Arrieta hopes to use Accelerate to save researchers money at the point of purchase. The program uses blockchain to simplify the process of acquisition.

“How many of you work with the federal government?” Arrieta asked the audience. “Do you get sick of reentering the same information over and over again? Every single business opportunity you apply for, you have to resubmit your financial information. You constantly have to check for validation and verification, constantly have to resubmit capabilities.”

Wouldn’t it be better to have historical notes available for each transaction? said Arrieta. This would allow clinical researchers to be able to focus on “the things they’re really good at,” instead of red tape.

“If we had the top cancer researcher in the world, would you really want her spending her time learning about federal regulations as to how to spend money, or do you want her trying to solve cancer?” Arrieta said. “What we’re doing is providing that data to the individual in a distributed manner so they can read the information of historical purchases that support activity, and they can focus on the objectives and risks they see as it relates to their programming and their objectives.”

Blockchain also creates transparency among researchers, Arrieta said, which says creates an “uncomfortable reality” in the fact that they have to make a decision regarding data, fundamentally changing value exchange.

“The beauty of our business model is internal investment,” Arrieta said. For instance, the HHS could take all the sepsis data that exists in their system, put it into a distributed ledger, and share it with an external source.

“Maybe that could fuel partnership,” Arrieta said. “I can make data available to researchers in the field in real-time so they can actually test their hypothesis, test their intuition, and test their imagination as it relates to solving real-world problems.”

 

Shivom is creating a genomic data hub to elongate human life with AI

From VentureBeat.com
Blockchain-based genomic data hub platform Shivom recently reached its $35 million hard cap within 15 seconds of opening its main token sale. Shivom received funding from a number of crypto VC funds, including Collinstar, Lateral, and Ironside.

The goal is to create the world’s largest store of genomic data while offering an open web marketplace for patients, data donors, and providers — such as pharmaceutical companies, research organizations, governments, patient-support groups, and insurance companies.

“Disrupting the whole of the health care system as we know it has to be the most exciting use of such large DNA datasets,” Shivom CEO Henry Ines told me. “We’ll be able to stratify patients for better clinical trials, which will help to advance research in precision medicine. This means we will have the ability to make a specific drug for a specific patient based on their DNA markers. And what with the cost of DNA sequencing getting cheaper by the minute, we’ll also be able to sequence individuals sooner, so young children or even newborn babies could be sequenced from birth and treated right away.”

While there are many solutions examining DNA data to explain heritage, intellectual capabilities, health, and fitness, the potential of genomic data has largely yet to be unlocked. A few companies hold the monopoly on genomic data and make sizeable profits from selling it to third parties, usually without sharing the earnings with the data donor. Donors are also not informed if and when their information is shared, nor do they have any guarantee that their data is secure from hackers.

Shivom wants to change that by creating a decentralized platform that will break these monopolies, democratizing the processes of sharing and utilizing the data.

“Overall, large DNA datasets will have the potential to aid in the understanding, prevention, diagnosis, and treatment of every disease known to mankind, and could create a future where no diseases exist, or those that do can be cured very easily and quickly,” Ines said. “Imagine that, a world where people do not get sick or are already aware of what future diseases they could fall prey to and so can easily prevent them.”

Shivom’s use of blockchain technology and smart contracts ensures that all genomic data shared on the platform will remain anonymous and secure, while its OmiX token incentivizes users to share their data for monetary gain.

Rise in Population Genomics: Local Government in India Will Use Blockchain to Secure Genetic Data

Blockchain will secure the DNA database for 50 million citizens in the eighth-largest state in India. The government of Andhra Pradesh signed a Memorandum of Understanding with a German genomics and precision medicine start-up, Shivom, which announced to start the pilot project soon. The move falls in line with a trend for governments turning to population genomics, and at the same time securing the sensitive data through blockchain.

Andhra Pradesh, DNA, and blockchain

Storing sensitive genetic information safely and securely is a big challenge. Shivom builds a genomic data-hub powered by blockchain technology. It aims to connect researchers with DNA data donors thus facilitating medical research and the healthcare industry.

With regards to Andhra Pradesh, the start-up will first launch a trial to determine the viability of their technology for moving from a proactive to a preventive approach in medicine, and towards precision health. “Our partnership with Shivom explores the possibilities of providing an efficient way of diagnostic services to patients of Andhra Pradesh by maintaining the privacy of the individual data through blockchain technologies,” said J A Chowdary, IT Advisor to Chief Minister, Government of Andhra Pradesh.

Other Articles in this Open Access Journal on Digital Health include:

Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

Medical Applications and FDA regulation of Sensor-enabled Mobile Devices: Apple and the Digital Health Devices Market

 

How Social Media, Mobile Are Playing a Bigger Part in Healthcare

 

E-Medical Records Get A Mobile, Open-Sourced Overhaul By White House Health Design Challenge Winners

 

Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI

 

Digital Health Breakthrough Business Models, June 5, 2018 @BIOConvention, Boston, BCEC

 

 

 

 

 

 

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Long interspersed nuclear elements 1 (LINE1) is repeated half a million times in the human genome, making up nearly a fifth of the DNA in every cell. But nobody cared to study it and may be the reason to call it junk DNA. LINE1, like other transposons (or “jumping genes”), has the unusual ability to copy and insert itself in random places in the genome. Many other research groups uncovered possible roles in early mouse embryos and in brain cells. But nobody quite established a proper report about the functions of LINE1.

 

Geneticists gave attention to LINE1 when it was found to cause cancer or genetic disorders like hemophilia. But researchers at University of California at San Francisco suspected there was more characteristics of LINE1. They suspected that if it can be most harmless then it can be worst harmful also.

 

Many reports showed that LINE1 is especially active inside developing embryos, which suggests that the segment actually plays a key role in coordinating the development of cells in an embryo. Researchers at University of California at San Francisco figured out how to turn LINE1 off in mouse embryos by blocking LINE1 RNA. As a result the embryos got stuck in the two-cell stage, right after a fertilized egg has first split. Without LINE1, embryos essentially stopped developing.

 

The researchers thought that LINE1 RNA particles act as molecular “glue,” bringing together a suite of molecules that switch off the two-cell stage and kick it into the next phase of development. In particular, it turns off a gene called Dux, which is active in the two-cell stage.

 

LINE1’s ability to copy itself, however, seems to have nothing to do with its role in embryonic development. When LINE1 was blocked from inserting itself into the genome, the embryonic stem cells remained unaffected. It’s possible that cells in embryos have a way of making LINE1 RNA while also preventing its potentially harmful “jumping” around in the genome. But it’s unlikely that every one of the thousands of copies of LINE1 is actually being used to regulate embryonic development.

 

LINE1 is abundant in the genomes of almost all mammals. Other transposons, also once considered junk DNA, have turned out to have critical roles in development in human cells too. There are differences between mice and humans, so, the next obvious step is to study LINE1 in human cells, where it makes up 17 percent of the genome.

 

References:

 

https://www-theatlantic-com.cdn.ampproject.org/c/s/www.theatlantic.com/amp/article/563354/

 

https://www.ncbi.nlm.nih.gov/pubmed/29937225

 

https://www.nature.com/scitable/topicpage/transposons-the-jumping-genes-518

 

https://www.sciencedaily.com/releases/2018/06/180621141038.htm

 

https://www.ncbi.nlm.nih.gov/pubmed/16015595

 

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Live Conference Coverage @Medcitynews Converge 2018 Philadelphia: The Davids vs. the Cancer Goliath Part 2

8:40 – 9:25 AM The Davids vs. the Cancer Goliath Part 2

Startups from diagnostics, biopharma, medtech, digital health and emerging tech will have 8 minutes to articulate their visions on how they aim to tame the beast.

Start Time End Time Company
8:40 8:48 3Derm
8:49 8:57 CNS Pharmaceuticals
8:58 9:06 Cubismi
9:07 9:15 CytoSavvy
9:16 9:24 PotentiaMetrics

Speakers:
Liz Asai, CEO & Co-Founder, 3Derm Systems, Inc. @liz_asai
John M. Climaco, CEO, CNS Pharmaceuticals @cns_pharma 

John Freyhof, CEO, CytoSavvy
Robert Palmer, President & CEO, PotentiaMetrics @robertdpalmer 
Moira Schieke M.D., Founder, Cubismi, Adjunct Assistant Prof UW Madison @cubismi_inc

 

3Derm Systems

3Derm Systems is an image analysis firm for dermatologic malignancies.  They use a tele-medicine platform to accurately triage out benign malignancies observed from the primary care physician, expediate those pathology cases if urgent to the dermatologist and rapidly consults with you over home or portable device (HIPAA compliant).  Their suite also includes a digital dermatology teaching resource including digital training for students and documentation services.

 

CNS Pharmaceuticals

developing drugs against CNS malignancies, spun out of research at MD Anderson.  They are focusing on glioblastoma and Berubicin, an anthracycline antiobiotic (TOPOII inhibitor) that can cross the blood brain barrier.  Berubicin has good activity in a number of animal models.  Phase I results were very positive and Phase II is scheduled for later in the year.  They hope that the cardiotoxicity profile is less severe than other anthracyclines.  The market opportunity will be in temazolamide resistant glioblastoma.

Cubismi

They are using machine learning and biomarker based imaging to visualize tumor heterogeneity. “Data is the new oil” (Intel CEO). We need prediction machines so they developed a “my body one file” system, a cloud based data rich file of a 3D map of human body.

CUBISMI IS ON A MISSION TO HELP DELIVER THE FUTURE PROMISE OF PRECISION MEDICINE TO CURE DISEASE AND ASSURE YOUR OPTIMAL HEALTH.  WE ARE BUILDING A PATIENT-DOCTOR HEALTH DATA EXCHANGE PLATFORM THAT WILL LEVERAGE REVOLUTIONARY MEDICAL IMAGING TECHNOLOGY AND PUT THE POWER OF HEALTH DATA INTO THE HANDS OF YOU AND YOUR DOCTORS.

 

CytoSavvy

CytoSavvy is a digital pathology company.  They feel AI has a fatal flaw in that no way to tell how a decision was made. Use a Shape Based Model Segmentation algorithm which uses automated image analysis to provide objective personalized pathology data.  They are partnering with three academic centers (OSU, UM, UPMC) and pool data and automate the rule base for image analysis.

CytoSavvy’s patented diagnostic dashboards are intuitive, easy–to-use and HIPAA compliant. Our patented Shape-Based Modeling Segmentation (SBMS) algorithms combine shape and color analysis capabilities to increase reliability, save time, and improve decisions. Specifications and capabilities for our web-based delivery system follow.

link to their white paper: https://www.cytosavvy.com/resources/healthcare-ai-value-proposition.pdf

PotentialMetrics

They were developing a diagnostic software for cardiology epidemiology measuring outcomes however when a family member got a cancer diagnosis felt there was a need for outcomes based models for cancer treatment/care.  They deliver real world outcomes for persoanlized patient care to help patients make decisions on there care by using a socioeconomic modeling integrated with real time clinical data.

Featured in the Wall Street Journal, using the informed treatment decisions they have generated achieve a 20% cost savings on average.  There research was spun out of Washington University St. Louis.

They have concentrated on urban markets however the CEO had mentioned his desire to move into more rural areas of the country as there models work well for patients in the rural setting as well.

Please follow on Twitter using the following #hash tags and @pharma_BI 

#MCConverge

#cancertreatment

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

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